startTime=100,
stopTime=2900,
label=6)
a.addDataFiles(fileSourceName="igor2.txt",
fileSourcePath="../",
startTime=600,
stopTime=6000,
label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="dataOnly")
clf = a.loadDumpClassifier("dataOnly")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="dataOnly")
from sklearn import svm
clf = svm.SVC(kernel='rbf')
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="dataOnly")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
startTime=100,
stopTime=2900,
label=6)
a.addDataFiles(fileSourceName="igor2.txt",
fileSourcePath="../",
startTime=600,
stopTime=6000,
label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="MLPClassifier")
clf = a.loadDumpClassifier("MLPClassifier")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="MLPClassifier")
from sklearn.neural_network import MLPClassifier
clf = MLPClassifier()
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="MLPClassifier")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)
label=5)
a.addDataFiles(fileSourceName="igor.txt",
fileSourcePath="../",
startTime=100,
stopTime=2900,
label=6)
a.addDataFiles(fileSourceName="igor2.txt",
fileSourcePath="../",
startTime=600,
stopTime=6000,
label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="KNeighborsClassifier")
clf = a.loadDumpClassifier("KNeighborsClassifier")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="KNeighborsClassifier")
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=4, metric='euclidean')
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="KNeighborsClassifier")
a.testClassifier()
a.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=2000, stopTime=6000, label=4)
a.addDataFiles(fileSourceName="markus.txt", fileSourcePath="../", startTime=500, stopTime=3300, label=5)
a.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=100, stopTime=2900, label=6)
a.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=600, stopTime=6000, label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="XGBClassifier")
clf = a.loadDumpClassifier("XGBClassifier")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="XGBClassifier")
from xgboost import XGBClassifier
clf = XGBClassifier()
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="XGBClassifier")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
a.addDataFiles(fileSourceName="ben.txt", fileSourcePath="../", startTime=2000, stopTime=6000, label=4)
a.addDataFiles(fileSourceName="markus.txt", fileSourcePath="../", startTime=500, stopTime=3300, label=5)
a.addDataFiles(fileSourceName="igor.txt", fileSourcePath="../", startTime=100, stopTime=2900, label=6)
a.addDataFiles(fileSourceName="igor2.txt", fileSourcePath="../", startTime=600, stopTime=6000, label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="DecisionTreeClassifier")
clf = a.loadDumpClassifier("DecisionTreeClassifier")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="DecisionTreeClassifier")
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="DecisionTreeClassifier")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
startTime=100,
stopTime=2900,
label=6)
a.addDataFiles(fileSourceName="igor2.txt",
fileSourcePath="../",
startTime=600,
stopTime=6000,
label=6)
a.readDataSet(equalLength=False, checkData=False)
useDump = False
if useDump:
a.loadDumpNormParam(dumpName="GaussianNB")
clf = a.loadDumpClassifier("GaussianNB")
a.testClassifier(classifier=clf)
a.setFileSink(fileSinkName="chris", fileSinkPath="../")
a.startLiveClassification()
else:
a.initFeatNormalization(dumpName="GaussianNB")
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
a.trainClassifier(classifier=clf)
a.dumpClassifier(dumpName="GaussianNB")
a.testClassifier()
windowedData, windowLabels = a.windowSplitSourceDataTT()
index = np.linspace(0, len(windowedData) - 1, len(windowedData), dtype=int)
random.shuffle(index)